Reference : Artificial surfaces characteristics and sediment connectivity explain muddy flood haz...
Scientific journals : Article
Life sciences : Agriculture & agronomy
Engineering, computing & technology : Multidisciplinary, general & others
http://hdl.handle.net/2268/212314
Artificial surfaces characteristics and sediment connectivity explain muddy flood hazard in Wallonia
English
de Walque, Baptiste mailto [> >]
Degré, Aurore mailto [Université de Liège > Ingénierie des biosystèmes (Biose) > Echanges Eau-Sol-Plantes >]
Maugnard, Alexandre mailto [> >]
Bielders, Charles mailto [> >]
2017
Catena
Elsevier
158
89-101
Yes (verified by ORBi)
International
0341-8162
0008-7769
[en] Muddy flood ; Sediment connectivity ; Loess belt ; Artificial surfaces ; Hazard prediction model
[en] Over the last decades, the off-site damages caused by muddy floods have been of growing concern throughout much of Western Europe, and particularly in Wallonia (Belgium). A reliable identification of locations with a high muddy flood hazard is thus a key issue in this context. The main objective of this study was therefore to build and evaluate a muddy flood hazard prediction model in order to assess the probability of occurrence of muddy floods at any specific location. A logistic regression approach was used to explain muddy flood occurrence using a database of 442 muddy flood-affected sites and an equal number of homologous non-flooded sites. Explanatory variables were related to geomorphology, land use, sediment production and sediment connectivity in the contributing area. The prediction quality of the model was then validated using an independent dataset composed of 48 pairs of flooded and non-flooded sites. The best muddy flood hazard assessment model required a total of 5 explanatory variables as inputs: the mean slope, a sediment connectivity index, as well as the proportion, spatial aggregation and proximity to the outlet of artificial surfaces. The model resulted in a prediction
quality of 76% (calibration dataset) and 81% (validation dataset). Including the characteristics of artificial surfaces substantially improved the model quality (p-values from 10−11 to 10−5). All three variables related to artificial surfaces showed negative correlations with the muddy flood hazard. The proportion of cropland was not included in the final model, but this variable was strongly inversely correlated to the proportion of artificial surfaces. Besides the characteristics of artificial surfaces, sediment connectivity also showed significant explanatory power (p-value of 10−12). A positive correlation between sediment connectivity and muddy flood hazard was found. Future muddy flood hazard models should therefore include both artificial surfaces characteristics and sediment connectivity-related information. Given the good prediction quality, the developed statistical model could be used as a reliable tool to prioritize sites at risk of muddy floods in order to install mitigation measures.
Ingénierie des bio‐systèmes - BIOSE
SPW
http://hdl.handle.net/2268/212314
10.1016/j.catena.2017.06.016

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